code: def test_pandas_for_bp(self):
      dataset = datasets.load_diabetes()
      target = dataset.target.reshape(-1, 1)
  
      input_scaler = preprocessing.MinMaxScaler()
      target_scaler = preprocessing.MinMaxScaler()
  
      n_features = dataset.data.shape[1]
      input_columns = ["column_" + str(i) for i in range(n_features)]
  
      pandas_data = pd.DataFrame(dataset.data, columns=input_columns)
      pandas_data["target"] = target_scaler.fit_transform(target)
      pandas_data[input_columns] = input_scaler.fit_transform(
          pandas_data[input_columns]
      )
  
      x_train, x_test, y_train, y_test = train_test_split(
          asfloat(pandas_data[input_columns]),
          asfloat(pandas_data["target"]),
          test_size=0.15,
      )
  
      bpnet = algorithms.GradientDescent(
          [
              layers.Input(10),
              layers.Sigmoid(30),
              layers.Sigmoid(1),
          ],
          batch_size=None,
      )
      bpnet.train(x_train, y_train, epochs=50)
      y_predict = bpnet.predict(x_test).reshape(-1, 1)
      y_test = y_test.reshape(-1, 1)
  
      error = objectives.rmsle(
          target_scaler.inverse_transform(y_test),
          target_scaler.inverse_transform(y_predict).round(),
      )
      error = self.eval(error)
      self.assertGreater(0.5, error)
  
file_path: neupy/tests/compatibilities/test_pandas.py
imports: import pandas
  from sklearn import datasets, preprocessing
  from sklearn.model_selection import train_test_split
  from neupy import algorithms, layers
  from neupy.utils import asfloat
  from neupy.algorithms.gd import objectives
  from base import BaseTestCase
label_desc: Use image node_id 1 for calling the PandasCompatibilityTestCase obj's underlying member method code with example usage: obj.test_pandas_for_bp() without return types